






'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase        =							logging.get_logger(__name__)

__lowerCAmelCase        =							{"vocab_file": "sentencepiece.bpe.model"}

__lowerCAmelCase        =							{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

__lowerCAmelCase        =							{
    "camembert-base": 5_12,
}

__lowerCAmelCase        =							"▁"




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : Dict      			=     VOCAB_FILES_NAMES
  _a  : int      			=     PRETRAINED_VOCAB_FILES_MAP
  _a  : Tuple      			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a  : int      			=     ['''input_ids''', '''attention_mask''']







  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__="<s>"   ,	UpperCamelCase__="</s>"   ,	UpperCamelCase__="</s>"   ,	UpperCamelCase__="<s>"   ,	UpperCamelCase__="<unk>"   ,	UpperCamelCase__="<pad>"   ,	UpperCamelCase__="<mask>"   ,	UpperCamelCase__=["<s>NOTUSED", "</s>NOTUSED"]   ,	UpperCamelCase__ = None   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else mask_token

         a_						     =			{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=UpperCamelCase__   ,	eos_token=UpperCamelCase__   ,	unk_token=UpperCamelCase__   ,	sep_token=UpperCamelCase__   ,	cls_token=UpperCamelCase__   ,	pad_token=UpperCamelCase__   ,	mask_token=UpperCamelCase__   ,	additional_special_tokens=UpperCamelCase__   ,	sp_model_kwargs=self.sp_model_kwargs   ,	**UpperCamelCase__   ,	)
         a_						     =			spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(str(UpperCamelCase__	)	)
         a_						     =			vocab_file
         # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
         # sentencepiece vocabulary (this is the case for <s> and </s>
         a_						     =			{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
         a_						     =			len(self.fairseq_tokens_to_ids	)
         a_						     =			len(self.sp_model	) + len(self.fairseq_tokens_to_ids	)
         a_						     =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         a_						     =			[self.cls_token_id]
         a_						     =			[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = False	):






         """simple docstring"""


         if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=UpperCamelCase__   ,	token_ids_a=UpperCamelCase__   ,	already_has_special_tokens=UpperCamelCase__	)

         if token_ids_a is None:
                return [1] + ([0] * len(UpperCamelCase__	)) + [1]
         return [1] + ([0] * len(UpperCamelCase__	)) + [1, 1] + ([0] * len(UpperCamelCase__	)) + [1]







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         a_						     =			[self.sep_token_id]
         a_						     =			[self.cls_token_id]

         if token_ids_a is None:
                return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]







  @property
  def    _a       (			self	):






         """simple docstring"""


         return len(self.fairseq_tokens_to_ids	) + len(self.sp_model	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			{self.convert_ids_to_tokens(UpperCamelCase__	): i for i in range(self.vocab_size	)}
         vocab.update(self.added_tokens_encoder	)
         return vocab







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         return self.sp_model.encode(UpperCamelCase__   ,	out_type=UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
         elif self.sp_model.PieceToId(UpperCamelCase__	) == 0:
                # Convert sentence piece unk token to fairseq unk token index
                return self.unk_token_id
         return self.fairseq_offset + self.sp_model.PieceToId(UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         if index in self.fairseq_ids_to_tokens:
                return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset	)







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			[]
         a_						     =			''
         a_						     =			False
         for token in tokens:
                # make sure that special tokens are not decoded using sentencepiece model
                if token in self.all_special_tokens:
                       if not prev_is_special:
                              out_string += " "
                       out_string += self.sp_model.decode(UpperCamelCase__	) + token
                       a_						     =			True
                       a_						     =			[]
                else:
                       current_sub_tokens.append(UpperCamelCase__	)
                       a_						     =			False
         out_string += self.sp_model.decode(UpperCamelCase__	)
         return out_string.strip()







  def __getstate__(			self	):






         """simple docstring"""


         a_						     =			self.__dict__.copy()
         a_						     =			None
         return state







  def __setstate__(			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			d

         # for backward compatibility
         if not hasattr(self   ,	'sp_model_kwargs'	):
                a_						     =			{}

         a_						     =			spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(self.vocab_file	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         if not os.path.isdir(UpperCamelCase__	):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'	)
                return
         a_						     =			os.path.join(
             UpperCamelCase__   ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(UpperCamelCase__	) and os.path.isfile(self.vocab_file	):
                copyfile(self.vocab_file   ,	UpperCamelCase__	)
         elif not os.path.isfile(self.vocab_file	):
                with open(UpperCamelCase__   ,	'wb'	) as fi:
                       a_						     =			self.sp_model.serialized_model_proto()
                       fi.write(UpperCamelCase__	)

         return (out_vocab_file,)












'''simple docstring'''







import os
import sys
import unittest


__lowerCAmelCase        =							os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


__lowerCAmelCase        =							os.path.join("tests", "models", "bert", "test_modeling_bert.py")
__lowerCAmelCase        =							os.path.join("tests", "models", "blip", "test_modeling_blip.py")




class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         a_						     =			get_test_to_tester_mapping(UpperCamelCase__	)
         a_						     =			get_test_to_tester_mapping(UpperCamelCase__	)

         a_						     =			{'BertModelTest': 'BertModelTester'}

         a_						     =			{
             'BlipModelTest': 'BlipModelTester',
             'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
             'BlipTextModelTest': 'BlipTextModelTester',
             'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
             'BlipVQAModelTest': 'BlipVQAModelTester',
             'BlipVisionModelTest': 'BlipVisionModelTester',
         }

         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)
         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			get_model_to_test_mapping(UpperCamelCase__	)
         a_						     =			get_model_to_test_mapping(UpperCamelCase__	)

         a_						     =			{
             'BertForMaskedLM': ['BertModelTest'],
             'BertForMultipleChoice': ['BertModelTest'],
             'BertForNextSentencePrediction': ['BertModelTest'],
             'BertForPreTraining': ['BertModelTest'],
             'BertForQuestionAnswering': ['BertModelTest'],
             'BertForSequenceClassification': ['BertModelTest'],
             'BertForTokenClassification': ['BertModelTest'],
             'BertLMHeadModel': ['BertModelTest'],
             'BertModel': ['BertModelTest'],
         }

         a_						     =			{
             'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
             'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
             'BlipForQuestionAnswering': ['BlipVQAModelTest'],
             'BlipModel': ['BlipModelTest'],
             'BlipTextModel': ['BlipTextModelTest'],
             'BlipVisionModel': ['BlipVisionModelTest'],
         }

         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)
         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			get_model_to_tester_mapping(UpperCamelCase__	)
         a_						     =			get_model_to_tester_mapping(UpperCamelCase__	)

         a_						     =			{
             'BertForMaskedLM': ['BertModelTester'],
             'BertForMultipleChoice': ['BertModelTester'],
             'BertForNextSentencePrediction': ['BertModelTester'],
             'BertForPreTraining': ['BertModelTester'],
             'BertForQuestionAnswering': ['BertModelTester'],
             'BertForSequenceClassification': ['BertModelTester'],
             'BertForTokenClassification': ['BertModelTester'],
             'BertLMHeadModel': ['BertModelTester'],
             'BertModel': ['BertModelTester'],
         }

         a_						     =			{
             'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
             'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
             'BlipForQuestionAnswering': ['BlipVQAModelTester'],
             'BlipModel': ['BlipModelTester'],
             'BlipTextModel': ['BlipTextModelTester'],
             'BlipVisionModel': ['BlipVisionModelTester'],
         }

         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)
         self.assertEqual(get_test_info.to_json(UpperCamelCase__	)   ,	UpperCamelCase__	)




